The goal of this project is to create systems that can interpret and understand day to day human experience. Achieving this goal will support many practical applications with broad social impact, including developing assistive technology for the disabled, creating models of patterns of human interaction for use in the social sciences, and creating new kinds of just-in-time information systems for business and personal use. There are three main themes to this research. First is work in knowledge representation on the structure of goals, plans, and actions. This project is concerned not just with plan synthesis, but also with supporting reasoning about the intentional basis of human action. Second is work on probabilistic reasoning, because any method for interpreting human behavior is necessarily fraught with uncertainty. This work draws upon and extends recent approaches on using model-counting algorithms for probabilistic inference, and methods for combining probability theory with first-order logic. Third is work on ubiquitous sensing: the idea that data from large numbers of simple, inexpensive sensors that directly measure properties of the world can be used to replace or augment complex input modalities such as machine vision or natural language understanding.
